Home
Introduction and Lectures
Intro to the Workshop and Core
Schedule
What is Bioinformatics/Genomics?
Experimental Design and Cost Estimation
Support
Slack
Zoom
Cheat Sheets
Software and Links
Scripts
Prerequisites
CLI
R
Data Reduction
Files and Filetypes
Project setup
Generating Expression Matrix
scRNAseq Analysis
Prepare scRNAseq Analysis
scRNAseq Analysis - PART1
scRNAseq Analysis - PART2
scRNAseq Analysis - PART3
scRNAseq Analysis - PART4
scRNAseq Analysis - PART5
scRNAseq Analysis - PART6
ETC
Closing thoughts
Workshop Photos
Github page
Biocore website

Last Updated: March 20 2022

Part 5:

Load libraries

library(Seurat)
library(ggplot2)
library(dplyr)

Load the Seurat object

load(file="pca_sample_corrected.RData")
experiment.aggregate

An object of class Seurat 36601 features across 3343 samples within 1 assay Active assay: RNA (36601 features, 3941 variable features) 1 dimensional reduction calculated: pca

So how many features should we use? Use too few and your leaving out interesting variation that may define cell types, use too many and you add in noise? maybe?

Lets choose the first 25, based on our prior part.

use.pcs = 1:25

Identifying clusters

Seurat implements an graph-based clustering approach. Distances between the cells are calculated based on previously identified PCs.

The default method for identifying k-nearest neighbors has been changed in V4 to annoy (“Approximate Nearest Neighbors Oh Yeah!). This is an approximate nearest-neighbor approach that is widely used for high-dimensional analysis in many fields, including single-cell analysis. Extensive community benchmarking has shown that annoy substantially improves the speed and memory requirements of neighbor discovery, with negligible impact to downstream results.

Seurat prior approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNAseq data. Briefly, Seurat identified clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors (KNN) and construct the SNN graph. Then optimize the modularity function to determine clusters. For a full description of the algorithms, see Waltman and van Eck (2013) The European Physical Journal B. You can switch back to using the previous default setting using nn.method=”rann”.

The FindClusters function implements the neighbor based clustering procedure, and contains a resolution parameter that sets the granularity of the downstream clustering, with increased values leading to a greater number of clusters. I tend to like to perform a series of resolutions, investigate and choose.

?FindNeighbors

experiment.aggregate <- FindNeighbors(experiment.aggregate, reduction="pca", dims = use.pcs)

experiment.aggregate <- FindClusters(
    object = experiment.aggregate,
    resolution = seq(0.25,4,0.5),
    verbose = FALSE
)

Seurat add the clustering information to the metadata beginning with RNA_snn_res. followed by the resolution

head(experiment.aggregate[[]])

orig.ident nCount_RNA nFeature_RNA percent.mito AAACCTGCAGACTCGC-conv_COVID conv_COVID 7321 2560 0.3824614 AAACGGGTCTGGGCCA-conv_COVID conv_COVID 6765 2128 0.8869180 AAACGGGTCTTAGAGC-conv_COVID conv_COVID 11471 2964 0.4794700 AAAGATGCATCCTAGA-conv_COVID conv_COVID 9610 2605 0.9261186 AAAGCAAAGAGTAAGG-conv_COVID conv_COVID 7242 2051 0.3314002 AAAGCAAAGCCTATGT-conv_COVID conv_COVID 1016 772 1.6732283 S.Score G2M.Score Phase old.ident AAACCTGCAGACTCGC-conv_COVID -0.04879952 -0.026197357 G1 conv_COVID AAACGGGTCTGGGCCA-conv_COVID -0.01441873 -0.031983183 G1 conv_COVID AAACGGGTCTTAGAGC-conv_COVID -0.05208459 -0.003787519 G1 conv_COVID AAAGATGCATCCTAGA-conv_COVID -0.02179640 -0.011175856 G1 conv_COVID AAAGCAAAGAGTAAGG-conv_COVID 0.11342246 0.019373865 S conv_COVID AAAGCAAAGCCTATGT-conv_COVID 0.03533120 -0.017761709 S conv_COVID RNA_snn_res.0.25 RNA_snn_res.0.75 RNA_snn_res.1.25 AAACCTGCAGACTCGC-conv_COVID 6 8 8 AAACGGGTCTGGGCCA-conv_COVID 0 0 0 AAACGGGTCTTAGAGC-conv_COVID 0 0 0 AAAGATGCATCCTAGA-conv_COVID 6 8 8 AAAGCAAAGAGTAAGG-conv_COVID 2 2 2 AAAGCAAAGCCTATGT-conv_COVID 5 7 7 RNA_snn_res.1.75 RNA_snn_res.2.25 RNA_snn_res.2.75 AAACCTGCAGACTCGC-conv_COVID 9 8 6 AAACGGGTCTGGGCCA-conv_COVID 3 1 13 AAACGGGTCTTAGAGC-conv_COVID 3 1 1 AAAGATGCATCCTAGA-conv_COVID 9 8 6 AAAGCAAAGAGTAAGG-conv_COVID 1 3 2 AAAGCAAAGCCTATGT-conv_COVID 5 7 5 RNA_snn_res.3.25 RNA_snn_res.3.75 seurat_clusters AAACCTGCAGACTCGC-conv_COVID 6 6 6 AAACGGGTCTGGGCCA-conv_COVID 10 11 11 AAACGGGTCTTAGAGC-conv_COVID 10 11 11 AAAGATGCATCCTAGA-conv_COVID 6 6 6 AAAGCAAAGAGTAAGG-conv_COVID 1 0 0 AAAGCAAAGCCTATGT-conv_COVID 4 5 5

Lets first investigate how many clusters each resolution produces and set it to the smallest resolutions of 0.5 (fewest clusters).

sapply(grep("res",colnames(experiment.aggregate@meta.data),value = TRUE),
       function(x) length(unique(experiment.aggregate@meta.data[,x])))

RNA_snn_res.0.25 RNA_snn_res.0.75 RNA_snn_res.1.25 RNA_snn_res.1.75 9 12 14 16 RNA_snn_res.2.25 RNA_snn_res.2.75 RNA_snn_res.3.25 RNA_snn_res.3.75 20 22 23 23

Plot TSNE coloring for each resolution

tSNE dimensionality reduction plots are then used to visualize clustering results. As input to the tSNE, you should use the same PCs as input to the clustering analysis.

experiment.aggregate <- RunTSNE(
  object = experiment.aggregate,
  reduction.use = "pca",
  dims = use.pcs,
  do.fast = TRUE)

DimPlot(object = experiment.aggregate, group.by=grep("res",colnames(experiment.aggregate@meta.data),value = TRUE)[1:4], ncol=2 , pt.size=3.0, reduction = "tsne", label = T)

DimPlot(object = experiment.aggregate, group.by=grep("res",colnames(experiment.aggregate@meta.data),value = TRUE)[5:8], ncol=2 , pt.size=3.0, reduction = "tsne", label = T)

Try exploring different PCS, so first 5, 10, 15, we used 25, what about 100? How does the clustering change?

Once complete go back to 1:25

Choosing a resolution

Lets set the default identity to a resolution of 1.25 and produce a table of cluster to sample assignments.

Idents(experiment.aggregate) <- "RNA_snn_res.1.25"
table(Idents(experiment.aggregate),experiment.aggregate$orig.ident)

conv_COVID conv_MMR conv_Tdap norm_COVID 0 180 217 176 2 1 114 186 121 0 2 93 156 113 0 3 1 1 0 331 4 9 0 5 284 5 97 63 50 3 6 59 87 56 0 7 49 74 67 0 8 57 65 64 0 9 55 61 69 0 10 57 71 47 0 11 21 49 21 0 12 0 3 70 0 13 1 0 0 38

Plot TSNE coloring by the slot ‘ident’ (default).

DimPlot(object = experiment.aggregate, pt.size=0.5, reduction = "tsne", label = T)

uMAP dimensionality reduction plot.

experiment.aggregate <- RunUMAP(
  object = experiment.aggregate,
  dims = use.pcs)

Plot uMap coloring by the slot ‘ident’ (default).

DimPlot(object = experiment.aggregate, pt.size=0.5, reduction = "umap", label = T)

Catagorical data can be plotted using the DimPlot function.

TSNE plot by cell cycle

DimPlot(object = experiment.aggregate, pt.size=0.5, group.by = "Phase", reduction = "umap" )

Try creating a table, of cluster x cell cycle

Can use feature plot to plot our read valued metadata, like nUMI, Feature count, and percent Mito

FeaturePlot can be used to color cells with a ‘feature’, non categorical data, like number of UMIs

FeaturePlot(experiment.aggregate, features = c('nCount_RNA'), pt.size=0.5)

and number of genes present

FeaturePlot(experiment.aggregate, features = c('nFeature_RNA'), pt.size=0.5)

percent mitochondrial

FeaturePlot(experiment.aggregate, features = c('percent.mito'), pt.size=0.5)

Building a phylogenetic tree relating the ‘average’ cell from each group in default ‘Ident’ (currently “RNA_snn_res.1.25”). Tree is estimated based on a distance matrix constructed in either gene expression space or PCA space.

Idents(experiment.aggregate) <- "RNA_snn_res.1.25"
experiment.aggregate <- BuildClusterTree(
  experiment.aggregate, dims = use.pcs)

PlotClusterTree(experiment.aggregate)

Create new trees of other data

Once complete go back to Res 1.25

DimPlot(object = experiment.aggregate, pt.size=0.5, label = TRUE, reduction = "umap")

DimPlot(experiment.aggregate, pt.size = 0.5, label = TRUE, reduction = "tsne")

Merging clusters

Merge Clustering results, so lets say clusters 0, and 9 are actually the same cell type and we don’t wish to separate them out as distinct clusters. Same with 1, 6, and 11.

experiment.merged = experiment.aggregate
Idents(experiment.merged) <- "RNA_snn_res.1.25"

experiment.merged <- RenameIdents(
  object = experiment.merged,
  '9' = '0',
  '6' = '1',
  '11' = '1'
)

table(Idents(experiment.merged))

0 1 2 3 4 5 7 8 10 12 13 760 714 362 333 298 213 190 186 175 73 39

DimPlot(object = experiment.merged, pt.size=0.5, label = T, reduction = "umap")

DimPlot(experiment.merged, pt.size = 0.5, label = TRUE, reduction = "tsne" )

VlnPlot(object = experiment.merged, features = "percent.mito", pt.size = 0.05)

Reording the clusters

In order to reorder the clusters for plotting purposes take a look at the levels of the Ident, which indicates the ordering, then relevel as desired.

experiment.examples <- experiment.merged
levels(experiment.examples@active.ident)

[1] "0" "1" "2" "3" "4" "5" "7" "8" "10" "12" "13"

experiment.examples@active.ident <- relevel(experiment.examples@active.ident, "12")
levels(experiment.examples@active.ident)

[1] "12" "0" "1" "2" "3" "4" "5" "7" "8" "10" "13"

# now cluster 12 is the "first" factor

DimPlot(object = experiment.examples, pt.size=0.5, label = T, reduction = "umap")

VlnPlot(object = experiment.examples, features = "percent.mito", pt.size = 0.05)

# relevel all the factors to the order I want
Idents(experiment.examples) <- factor(experiment.examples@active.ident, levels=c("12","3","4","13","0","1","2","5","7", "8", "10"))
levels(experiment.examples@active.ident)

[1] "12" "3" "4" "13" "0" "1" "2" "5" "7" "8" "10"

DimPlot(object = experiment.examples, pt.size=0.5, label = T, reduction = "umap")

Re-assign clustering result (subclustering only cluster 0) to clustering for resolution 3.75 (@ reslution 0.25) [adding a R prefix]

newIdent = as.character(Idents(experiment.examples))
newIdent[newIdent == '0'] = paste0("R",as.character(experiment.examples$RNA_snn_res.3.75[newIdent == '0']))

Idents(experiment.examples) <- as.factor(newIdent)
table(Idents(experiment.examples))

1 10 12 13 2 3 4 5 7 8 R10 R11 R14 R15 R17 R2 R20 R5 714 175 73 39 362 333 298 213 190 186 174 112 147 13 104 204 5 1

DimPlot(object = experiment.examples, pt.size=0.5, label = T, reduction = "umap")

Plot UMAP coloring by the slot ‘orig.ident’ (sample names) with alpha colors turned on. A pretty picture

DimPlot(object = experiment.aggregate, group.by="orig.ident", pt.size=0.5, reduction = "umap", shuffle = TRUE)

## Pretty umap using alpha
alpha.use <- 2/5
p <- DimPlot(object = experiment.aggregate, group.by="orig.ident", pt.size=0.5, reduction = "umap", shuffle = TRUE)
p$layers[[1]]$mapping$alpha <- alpha.use
p + scale_alpha_continuous(range = alpha.use, guide = F)

Removing cells assigned to clusters from a plot, So here plot all clusters but cluster 6 (contaminant?)

# create a new tmp object with those removed
experiment.aggregate.tmp <- experiment.aggregate[,-which(Idents(experiment.aggregate) %in% c("7"))]

dim(experiment.aggregate)

[1] 36601 3343

dim(experiment.aggregate.tmp)

[1] 36601 3153

DimPlot(object = experiment.aggregate.tmp, pt.size=0.5, reduction = "umap", label = T)

Identifying Marker Genes

Seurat can help you find markers that define clusters via differential expression.

FindMarkers identifies markers for a cluster relative to all other clusters.

FindAllMarkers does so for all clusters

FindAllMarkersNode defines all markers that split a Node from the cluster tree

?FindMarkers

markers = FindMarkers(experiment.aggregate, ident.1=c(3,7), ident.2 = c(4,5))

head(markers)

p_val avg_log2FC pct.1 pct.2 p_val_adj LTB 2.419412e-80 -2.5719909 0.031 0.577 8.855289e-76 RPS5 7.936504e-80 -0.9521245 0.901 0.990 2.904840e-75 RPL9 1.516769e-78 -0.8857788 0.906 0.990 5.551526e-74 MT-CO1 1.219309e-77 1.0513960 0.996 0.988 4.462794e-73 RPLP1 6.747611e-75 -0.7309051 0.998 1.000 2.469693e-70 RPS13 2.246155e-74 -0.8512602 0.956 0.998 8.221151e-70

dim(markers)

[1] 1541 5

table(markers$avg_log2FC > 0)

FALSE TRUE 430 1111

table(markers$p_val_adj < 0.05 & markers$avg_log2FC > 0)

FALSE TRUE 1455 86

pct.1 and pct.2 are the proportion of cells with expression above 0 in ident.1 and ident.2 respectively. p_val is the raw p_value associated with the differntial expression test with adjusted value in p_val_adj. avg_logFC is the average log fold change difference between the two groups.

avg_diff (lines 130, 193 and) appears to be the difference in log(x = mean(x = exp(x = x) - 1) + 1) between groups. It doesn’t seem like this should work out to be the signed ratio of pct.1 to pct.2 so I must be missing something. It doesn’t seem to be related at all to how the p-values are calculated so maybe it doesn’t matter so much, and the sign is probably going to be pretty robust to how expression is measured.

Can use a violin plot to visualize the expression pattern of some markers

VlnPlot(object = experiment.aggregate, features = rownames(markers)[1:2], pt.size = 0.05)

Or a feature plot

FeaturePlot(
    experiment.aggregate,
    "KLRD1",
    cols = c("lightgrey", "blue"),
    ncol = 2
)

FindAllMarkers can be used to automate the process across all genes.

markers_all <- FindAllMarkers(
    object = experiment.merged,
    only.pos = TRUE,
    min.pct = 0.25,
    thresh.use = 0.25
)
dim(markers_all)

[1] 5189 7

head(markers_all)

p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene ENO1 1.237638e-209 1.0391920 0.999 0.885 4.529880e-205 0 ENO1 RAN 8.163561e-195 1.0298925 0.995 0.852 2.987945e-190 0 RAN NPM1 2.481907e-169 0.8394197 1.000 0.961 9.084027e-165 0 NPM1 HSPE1 1.341949e-160 1.0955851 0.979 0.680 4.911667e-156 0 HSPE1 YBX1 4.112405e-148 0.8617766 0.995 0.878 1.505181e-143 0 YBX1 RPL35 1.065975e-146 0.7508455 0.997 0.925 3.901576e-142 0 RPL35

table(table(markers_all$gene))

1 2 3 4 5 6 1530 759 422 149 51 4

markers_all_single <- markers_all[markers_all$gene %in% names(table(markers_all$gene))[table(markers_all$gene) == 1],]

dim(markers_all_single)

[1] 1530 7

table(table(markers_all_single$gene))

1 1530

table(markers_all_single$cluster)

0 1 2 3 4 5 7 8 10 12 13 170 236 40 44 118 101 88 75 29 586 43

head(markers_all_single)

p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene SERBP1 1.258848e-120 0.8213553 0.976 0.762 4.607511e-116 0 SERBP1 GNG8 7.449926e-91 0.7430131 0.330 0.058 2.726747e-86 0 GNG8 FOSL1 3.206164e-89 0.7132977 0.775 0.346 1.173488e-84 0 FOSL1 CCT2 6.723697e-88 0.7162080 0.961 0.670 2.460940e-83 0 CCT2 MRPL12 2.166021e-87 0.7609562 0.809 0.397 7.927854e-83 0 MRPL12 CENPV 9.692308e-85 0.5072235 0.564 0.182 3.547482e-80 0 CENPV

Plot a heatmap of genes by cluster for the top 10 marker genes per cluster

top10 <- markers_all_single %>% group_by(cluster) %>% top_n(10, avg_log2FC)
DoHeatmap(
    object = experiment.merged,
    features = top10$gene
)

# Get expression of genes for cells in and out of each cluster
getGeneClusterMeans <- function(gene, cluster){
  x <- GetAssayData(experiment.merged)[gene,]
  m <- tapply(x, ifelse(Idents(experiment.merged) == cluster, 1, 0), mean)
  mean.in.cluster <- m[2]
  mean.out.of.cluster <- m[1]
  return(list(mean.in.cluster = mean.in.cluster, mean.out.of.cluster = mean.out.of.cluster))
}

## for sake of time only using first six (head)
means <- mapply(getGeneClusterMeans, head(markers_all[,"gene"]), head(markers_all[,"cluster"]))
means <- matrix(unlist(means), ncol = 2, byrow = T)

colnames(means) <- c("mean.in.cluster", "mean.out.of.cluster")
rownames(means) <- head(markers_all[,"gene"])
markers_all2 <- cbind(head(markers_all), means)
head(markers_all2)

Finishing up clusters.

At this point in time you should use the tree, markers, domain knowledge, and goals to finalize your clusters. This may mean adjusting PCA to use, mergers clusters together, choosing a new resolutions, etc. When finished you can further name it cluster by something more informative. Ex.

experiment.clusters <- experiment.aggregate
experiment.clusters <- RenameIdents(
  object = experiment.clusters,
  '0' = 'cell_type_A',
  '1' = 'cell_type_B',
  '2' = 'cell_type_C'
)
# and so on

DimPlot(object = experiment.clusters, pt.size=0.5, label = T, reduction = "tsne")

Right now our results ONLY exist in the Ident data object, lets save it to our metadata table so we don’t accidentally loose it.

experiment.merged$finalcluster <- Idents(experiment.merged)
head(experiment.merged[[]])

table(experiment.merged$finalcluster, experiment.merged$orig.ident)

conv_COVID conv_MMR conv_Tdap norm_COVID 0 235 278 245 2 1 194 322 198 0 2 93 156 113 0 3 1 1 0 331 4 9 0 5 284 5 97 63 50 3 7 49 74 67 0 8 57 65 64 0 10 57 71 47 0 12 0 3 70 0 13 1 0 0 38

Subsetting samples and plotting

If you want to look at the representation of just one sample, or sets of samples

experiment.sample1 <- subset(experiment.merged, orig.ident == "conv_COVID")

DimPlot(object = experiment.sample1, group.by = "RNA_snn_res.0.25", pt.size=0.5, label = TRUE, reduction = "tsne")

Adding in a new metadata column representing samples within clusters. So differential expression of PBMC2 vs PBMC3 within cluster 7

experiment.merged$samplecluster = paste(experiment.merged$orig.ident,experiment.merged$finalcluster,sep = '-')

# set the identity to the new variable
Idents(experiment.merged) <- "samplecluster"

markers.comp <- FindMarkers(experiment.merged, ident.1 = c("conv_COVID-0","conv_MMR-0"), ident.2= "conv_Tdap-0")

head(markers.comp)

p_val avg_log2FC pct.1 pct.2 p_val_adj MX1 2.899403e-12 0.6841773 0.238 0.033 1.061210e-07 IFI6 1.505343e-09 0.4599844 0.197 0.033 5.509704e-05 IRF7 3.922838e-09 0.5680142 0.267 0.086 1.435798e-04 IRF9 2.554919e-07 0.4152099 0.495 0.314 9.351260e-03 PLSCR1 3.368926e-07 0.3033803 0.238 0.086 1.233061e-02 TRIM22 1.483418e-06 0.4121095 0.253 0.102 5.429457e-02

experiment.subset <- subset(experiment.merged, samplecluster %in%  c( "conv_COVID-0", "conv_MMR-0", "conv_Tdap-0" ))
DoHeatmap(experiment.subset, features = head(rownames(markers.comp),20))

Idents(experiment.merged) <- "finalcluster"

And last lets save all the Seurat objects in our session.

save(list=grep('experiment', ls(), value = TRUE), file="clusters_seurat_object.RData")

Get the next Rmd file

download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2022-March-Single-Cell-RNA-Seq-Analysis/main/data_analysis/scRNA_Workshop-PART6.Rmd", "scRNA_Workshop-PART6.Rmd")

Session Information

sessionInfo()

R version 4.1.2 (2021-11-01) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Monterey 12.0.1 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] dplyr_1.0.8 ggplot2_3.3.5 SeuratObject_4.0.4 Seurat_4.1.0 loaded via a namespace (and not attached): [1] Rtsne_0.15 colorspace_2.0-3 deldir_1.0-6 [4] ellipsis_0.3.2 ggridges_0.5.3 rstudioapi_0.13 [7] spatstat.data_2.1-2 farver_2.1.0 leiden_0.3.9 [10] listenv_0.8.0 ggrepel_0.9.1 RSpectra_0.16-0 [13] fansi_1.0.2 codetools_0.2-18 splines_4.1.2 [16] knitr_1.37 polyclip_1.10-0 jsonlite_1.8.0 [19] ica_1.0-2 cluster_2.1.2 png_0.1-7 [22] uwot_0.1.11 shiny_1.7.1 sctransform_0.3.3 [25] spatstat.sparse_2.1-0 compiler_4.1.2 httr_1.4.2 [28] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0 [31] lazyeval_0.2.2 limma_3.50.1 cli_3.2.0 [34] later_1.3.0 htmltools_0.5.2 tools_4.1.2 [37] igraph_1.2.11 gtable_0.3.0 glue_1.6.2 [40] RANN_2.6.1 reshape2_1.4.4 Rcpp_1.0.8.3 [43] scattermore_0.8 jquerylib_0.1.4 vctrs_0.3.8 [46] ape_5.6-2 nlme_3.1-155 lmtest_0.9-39 [49] spatstat.random_2.1-0 xfun_0.30 stringr_1.4.0 [52] globals_0.14.0 mime_0.12 miniUI_0.1.1.1 [55] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3 [58] future_1.24.0 MASS_7.3-55 zoo_1.8-9 [61] scales_1.1.1 spatstat.core_2.4-0 promises_1.2.0.1 [64] spatstat.utils_2.3-0 parallel_4.1.2 RColorBrewer_1.1-2 [67] yaml_2.3.5 reticulate_1.24 pbapply_1.5-0 [70] gridExtra_2.3 sass_0.4.0 rpart_4.1.16 [73] stringi_1.7.6 highr_0.9 rlang_1.0.2 [76] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.15 [79] lattice_0.20-45 ROCR_1.0-11 purrr_0.3.4 [82] tensor_1.5 labeling_0.4.2 patchwork_1.1.1 [85] htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.2 [88] parallelly_1.30.0 RcppAnnoy_0.0.19 plyr_1.8.6 [91] magrittr_2.0.2 R6_2.5.1 generics_0.1.2 [94] DBI_1.1.2 withr_2.5.0 mgcv_1.8-39 [97] pillar_1.7.0 fitdistrplus_1.1-8 survival_3.3-1 [100] abind_1.4-5 tibble_3.1.6 future.apply_1.8.1 [103] crayon_1.5.0 KernSmooth_2.23-20 utf8_1.2.2 [106] spatstat.geom_2.3-2 plotly_4.10.0 rmarkdown_2.13 [109] grid_4.1.2 data.table_1.14.2 digest_0.6.29 [112] xtable_1.8-4 tidyr_1.2.0 httpuv_1.6.5 [115] munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1

☰ Menu

Single Cell RNA-Seq Analysis